In the past few years, graph neural networks (GNNs) have emerged as
a powerful technical approach for graph representation learning [1]}, [2]}.
By leveraging graph structure as well as node and edge features, GNNs can effectively learn low-dimensional representation vectors for each node or the entire graph.
However, to apply GNNs to downstream applications, it
usually requires abundant task-specific labeled data,
which can be extremely scarce in practice.
To alleviate the data scarcity issue [3]}, pre-training GNNs [4]}, [5]} has been proposed as a promising solution.
It first learns a transferable and expressive GNN
on a large number of unlabeled graphs or coarse-grained labeled graphs.
Then, the pre-trained GNN is fine-tuned on downstream datasets with task-specific labels.
